185 citations · 363 across the 17 of their papers we have counts for
9 papers · 1 filter
A Topological Filter for Learning with Label Noise
Pengxiang Wu, Songzhu Zheng, Mayank Goswami +2
Noisy labels can impair the performance of deep neural networks. To tackle this problem, in this paper, we propose a new method for filtering label noise. Unlike most existing meth…
Error-Bounded Correction of Noisy Labels
Songzhu Zheng, Pengxiang Wu, Aman Goswami +3
To collect large scale annotated data, it is inevitable to introduce label noise, i.e., incorrect class labels. To be robust against label noise, many successful methods rely on th…
Oriented Object Detection in Aerial Images with Box Boundary-Aware Vectors
Jingru Yi, Pengxiang Wu, Bo Liu +3
Oriented object detection in aerial images is a challenging task as the objects in aerial images are displayed in arbitrary directions and are usually densely packed. Current orien…
Enhanced MRI Reconstruction Network using Neural Architecture Search
Qiaoying Huang, Dong Yang, Yikun Xian +4
The accurate reconstruction of under-sampled magnetic resonance imaging (MRI) data using modern deep learning technology, requires significant effort to design the necessary comple…
PC-U Net: Learning to Jointly Reconstruct and Segment the Cardiac Walls in 3D from CT Data
Meng Ye, Qiaoying Huang, Dong Yang +4
The 3D volumetric shape of the heart's left ventricle (LV) myocardium (MYO) wall provides important information for diagnosis of cardiac disease and invasive procedure navigation.…
Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images
Hui Qu, Pengxiang Wu, Qiaoying Huang +7
Nuclei segmentation is a fundamental task in histopathology image analysis. Typically, such segmentation tasks require significant effort to manually generate accurate pixel-wise a…
MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird's Eye View Maps
Pengxiang Wu, Siheng Chen, Dimitris Metaxas
The ability to reliably perceive the environmental states, particularly the existence of objects and their motion behavior, is crucial for autonomous driving. In this work, we prop…
Generative Deep Learning Model for a Multi-level Nano-Optic Broadband Power Splitter
Yingheng Tang, Keisuke Kojima, Toshiaki Koike-Akino +6
We propose a novel Conditional Variational Autoencoder (CVAE) model, enhanced with adversarial censoring and active learning, for the generation of 550 nm broad bandwidth (1250 nm…
Vertebra-Focused Landmark Detection for Scoliosis Assessment
Jingru Yi, Pengxiang Wu, Qiaoying Huang +2
Adolescent idiopathic scoliosis (AIS) is a lifetime disease that arises in children. Accurate estimation of Cobb angles of the scoliosis is essential for clinicians to make diagnos…